Comparative Study for Classification Algorithms Performance in Crop Yields Prediction Systems

Ismael, Halbast Rashid; Abdulazeez, Adnan Mohsin; Hasan, Dathar Abas

doi:10.48161/qaj.v1n2a54

Cited by 4 publications

(2 citation statements)

References 38 publications

(30 reference statements)

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“…These classifiers, underpinned by algorithms that learn from and make predictions on data, are fundamental to a wide array of applications, from natural language processing to image recognition and beyond [32], [33]. The efficacy of a machine learning classifier lies in its ability to discern and learn from complex and often non-linear relationships within data, enabling it to adapt and improve over time with exposure to more information [34], [35].…”

Section: Machine Learning Classifiermentioning

confidence: 99%

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

Almufti,

Abdulazeez

2024

ijcs

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Gesture-based systems have emerged as a prominent breakthrough in the field of smart access control, effectively integrating security measures with user comfort. This study presents a novel gesture detection framework for smart entry systems that combines the computational capabilities of a Random Forest Classifier with the practicality of Arduino-based hardware. Central to methodology is the utilization of MediaPipe, an advanced computer vision library, to extract hand motion landmarks from live video streams. The selected landmarks function as a comprehensive dataset for training a Random Forest Classifier, which has been specifically chosen due to its high level of accuracy and efficiency in managing intricate classification jobs. The model exhibits outstanding competence in the categorization of gestures in real-time, attaining high levels of accuracy that are crucial for ensuring dependable entrance control. The Arduino microcontroller plays a vital role in the execution of the entry mechanism as it serves as the intermediary between the gesture detection software and the tangible entry control hardware. The incorporation of gesture recognition technology facilitates a cohesive and prompt user experience, wherein identified motions are directly converted into input commands. The system's practical use is demonstrated through a series of detailed tests, which highlight its dependability and efficiency across diverse climatic circumstances. The findings underscore the system's capacity as a flexible and safe solution for contactless access in many environments, including both private homes and highly protected establishments. Furthermore, the study makes a substantial contribution to the larger domain of human-computer interaction by showcasing the practicality of advanced gesture detection systems in many everyday contexts. The suggested framework presents a novel approach to smart entry systems and also paves the way for further investigation in the domains of smart home automation and interactive systems. In these areas, gesture-based interfaces have the potential to deliver user experiences that are both intuitive and efficient.

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Section: Machine Learning Classifiermentioning

confidence: 99%

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

Almufti,

Abdulazeez

2024

ijcs

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“…Dillman et al [34] indicate that the impact of rewards on project acceptance probably depends on several factors, such as the type of reward, its size, perceived risks in the design, previous experience with similar projects, and whether the compensation is collective for the community or individual. For example, monetary rewards are often associated with the so-called "bribe effect", where stakeholders feel they have been "bought" to solve a problem and research demonstrates its lack of effectiveness in increasing project acceptability [35][36][37]. Rather, investments in local projects that promote public welfare, and efforts to preserve or restore the environment, appear to be more valuable and have a more positive impact on the local community [38][39][40][41].…”

Section: Haraldsson Et Al Indicate That Climate Change and Environmen...mentioning

confidence: 99%

Algorithm of Development for Stabilization of Socio-Ecological Situation in Karakalpakstan

Abdullaev

2024

QAJ

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The relevance of developing a scientific framework for stabilizing the socio-ecological situation of the Republic of Karakalpakstan is determined by the need to address the environmental, social and economic challenges faced by the region. It is designed to improve the population’s quality of life, sustainable development and balanced interaction with the environment. The purpose of the research was to establish the relationship between social and environmental factors, to establish a mathematical model based on them, which will enable predicting future development and establishing a set of recommendations to improve the situation in the Republic of Karakalpakstan. The methods of theoretical analysis and mathematical modelling were used. The research outlines modern ideas about modelling the dynamics of socio-ecological processes in the example of the Republic of Karakalpakstan. It is established that the Republic has several problems, the solution of which should be started shortly. Considerable attention is paid to the setting of tasks and using for this purpose methods of system analysis of socio-ecological factors. In this work, the analysis of factors affecting the social and environmental environment of the Republic of Karakalpakstan using regression analysis is considered. The parameters of the developed mathematical model and their adequacy are determined. The developed mathematical model will allow identification of the social and ecological situation of the republic. At the end of the work, ten priority points for the next 5-10 years to improve the socio-ecological situation were presented. The practical value of the results is implemented in promoting the identification of new powerful reserves for further improvement of the management of social processes. This approach allows for increasing the national economic efficiency of the region and ensuring a balance between economic, social and environmental aspects of development, which is an essential step towards a sustainable future.

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A new robust hybrid model based on support vector machine and firefly meta-heuristic algorithm to predict pistachio yields and select effective soil variables

Seyedmohammadi

Zeinadini

Navidi

et al. 2023

Ecological Informatics

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Comparative Study for Classification Algorithms Performance in Crop Yields Prediction Systems

Cited by 4 publications

References 38 publications

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

Algorithm of Development for Stabilization of Socio-Ecological Situation in Karakalpakstan

A new robust hybrid model based on support vector machine and firefly meta-heuristic algorithm to predict pistachio yields and select effective soil variables

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